<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
    <atom:link href="https://rip.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
    <description></description>
    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
    </image>
    <item>
      <title>Shifts in Work Arrangements and Travel Behavior: A Longitudinal Analysis of Heterogeneous Effects of Teleworking on Commuting and Non-Commuting Travel Based on Seattle Commute Surveys</title>
      <link>https://rip.trb.org/View/2780211</link>
      <description><![CDATA[Following the official end of the COVID-19 pandemic in May 2023, employer policies regarding work arrangements have continued to change dynamically. This proposed project will draw on unique data from the 2022 and 2024 Seattle Commute Surveys to analyze how shifts in work arrangements from the pandemic to the post-pandemic era heterogeneously shape employees' commuting and non-commuting travel frequencies and mode choices, using econometric models, machine learning models, and explainable artificial intelligence (AI) methods. The research team will examine individual employees' post-pandemic work arrangements and patterns of change in commuting and non-commuting travel. The research will address three primary questions: (1) How have employees' work arrangements and travel patterns shifted from the pandemic to the post-pandemic era? (2) What is the relationship between work arrangement shifts and commuting mode choices? In what ways does the relationship vary across commuting distance strata and residential location types? (3) How are work arrangement shifts connected to non-commuting travel frequency and mode choices? Are these connections heterogeneous across employees' residential locations and commuting patterns? By employing econometric and AI models, this study aims to generate critical insights for transportation demand management and policy-making.]]></description>
      <pubDate>Fri, 18 Sep 2026 10:49:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/2780211</guid>
    </item>
    <item>
      <title>An Examination of the Adoption and Frequency of Part-Day and Full-Day Telework and the Impacts on Weekly Commute VMT</title>
      <link>https://rip.trb.org/View/2780205</link>
      <description><![CDATA[The COVID-19 pandemic has fundamentally reshaped work arrangements, creating long-term implications for transportation system performance, infrastructure planning, and demand management policy. While telework has received considerable attention, most existing research treats telework as a binary outcome, assuming that workers either commute to a workplace or remain at home for an entire day. This assumption does not reflect current behavior. A growing share of workers now engage in hybrid arrangements that include part-day telework, in which work is split between in-person and remote locations within a single day. This form of telework introduces additional flexibility in work scheduling and travel timing that is not represented in current transportation data systems or forecasting tools.

The distinction between part-day and full-day telework is important for transportation system performance. Full-day telework eliminates commute trips for a given day and directly reduces travel demand. In contrast, part-day telework typically retains commute travel but has the potential to shift its timing, redistribute trips across the day, and alter the temporal structure of travel demand. These effects are directly relevant to peak-period congestion, network reliability, and system efficiency. Despite these implications, major national data systems, including the American Community Survey and the National Household Travel Survey, do not adequately capture part-day telework behavior. As a result, current travel demand models may misrepresent both the level and timing of travel demand under hybrid work conditions, limiting their usefulness for evaluating transportation policies.

Motivated by the need to improve the behavioral realism and policy relevance of travel demand modeling in a post-pandemic environment, this study will examine the adoption and frequency of part-day and full-day telework using multi-day travel diary data from the 2023 Puget Sound Regional Household Travel Survey. These data provide detailed information on daily work location choices and activity participation over multiple days, enabling analysis of weekly travel patterns. The project will also establish defensible criteria for identifying part-day telework, addressing a key measurement issue that affects both survey design and model specification. The project will develop a joint econometric modeling framework that represents telework adoption, the number of part-day telework days, the number of full-day telework days, and commute distance. This joint structure is necessary to capture unobserved behavioral factors that simultaneously influence work arrangement choices and travel behavior, including preferences for flexibility, occupational constraints, and built environment characteristics. By explicitly modeling these interdependencies, the project will provide a more accurate representation of how work arrangements influence both the magnitude and timing of travel demand. In this regard, a central contribution of the project will be the translation of daily work location decisions into weekly commute vehicle miles of travel (VMT). By focusing on weekly activity-travel patterns, the project produces outputs that are directly relevant for evaluating congestion, system utilization, and the effectiveness of demand management strategies. The project will also examine variation in]]></description>
      <pubDate>Fri, 18 Sep 2026 10:27:18 GMT</pubDate>
      <guid>https://rip.trb.org/View/2780205</guid>
    </item>
    <item>
      <title>Capital Investment, Financing, Flood Risk, and Transportation Safety in the MidAmerica Region
</title>
      <link>https://rip.trb.org/View/2706033</link>
      <description><![CDATA[Transportation agencies in the MidAmerica region face increasing pressure to manage aging infrastructure under fiscal constraints while improving transportation safety. Rural highways, freight-intensive corridors, and aging bridges experience elevated crash severity, yet capital investment timing and financing decisions are rarely evaluated through a safety-risk lens.
This project develops an integrated empirical and probabilistic framework to quantify how capital investment timing, financing mechanisms, and flood-related hazards influence lifecycle transportation safety outcomes. The study constructs project- and asset-level datasets linking capital programming records, delivery timelines, financing mechanisms, infrastructure characteristics, crash outcomes, and flood risk indicators. Econometric models estimate statistical relationships between investment timing and safety performance. Monte Carlo simulation propagates uncertainty in delivery delays, cost escalation, traffic growth, and flood exposure to produce distributions of lifecycle safety risk and cost. Results will support safety-oriented capital planning and risk-informed decision-making for transportation agencies in the MidAmerica region.

]]></description>
      <pubDate>Sat, 23 May 2026 17:36:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2706033</guid>
    </item>
    <item>
      <title>Emerging Econometric and Data Collection Methods for Capturing Attitudinal and Social Factors in Activity and Travel Behavior Modeling</title>
      <link>https://rip.trb.org/View/1983973</link>
      <description><![CDATA[This study presents a survey designed to test for correlations between social resources and leisure activity behavior. An outcome was tested, leisure activity participation variety, which showed positive correlations with instrumental and expressive social resource access. This work motivates the use of this survey instrument with a larger sample to increase the inferential strength of this social capital theory in explaining leisure activity behavior. It is important to note that although the Qualtrics Panel sample had the greatest issue with validity, this was partially due to greater mobile device usage. The greater diversity of that sample may outweigh this and careful consideration of survey design for mobile devices is warranted.]]></description>
      <pubDate>Tue, 21 Jun 2022 09:10:00 GMT</pubDate>
      <guid>https://rip.trb.org/View/1983973</guid>
    </item>
    <item>
      <title>
Economic Impacts of Transportation Infrastructure Investments
</title>
      <link>https://rip.trb.org/View/1903393</link>
      <description><![CDATA[The purpose of this project is to identify optimal transportation investment strategies that maximize the economic returns for the entire Mid-Atlantic region, considering both traditional and technological investments. Toward this end, the study has four main objectives:
(1) Compile a unique database of county-level economic outcomes, historic annual transportation investments by type, including both traditional (i.e., construction and maintenance of roads) and technological (e.g., fiber optic cables for infrastructure). This database will be assembled from multiple sources including U.S. and local Census data, Departments of Transportation (DOTs) of states in the study region,
and local governments.
(2) Develop a spatial econometric model that describes multiple economic indicators at the geographic resolution of a county as a function of different types of transportation investments made within the region, after controlling for key socio-demographics.
(3) Explore and quantify the moderating influence of urban land-use patterns and transportation network connectivity on the impact of transportation investments.
(4) Quantify the direct (i.e., change in economic outcomes in a county due to changes in transportation investments in the same county), indirect (change in economic
outcomes in a county due to changes in transportation investments in neighboring counties), and total effect of transportation investments on economic outcomes.
]]></description>
      <pubDate>Tue, 11 Jan 2022 16:51:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/1903393</guid>
    </item>
    <item>
      <title>Mobility and sustainability in the post-pandemic reopening phase</title>
      <link>https://rip.trb.org/View/1864509</link>
      <description><![CDATA[This project will study how the COVID-19 pandemic has affected both travel and pro-environmental preferences and how commuters interact with transportation infrastructure, with particular focus on sustainable transportation modes and how transit preferences evolve during the pandemic receding stage. A multi-wave survey of commuters will capture changes in behavior and priorities and how attitudes toward the environment and diseases shape mobility preferences over time. The collected data will be used to train a microeconometric model of welfare changes under the “new normal” of the mobility ecosystem.]]></description>
      <pubDate>Thu, 08 Jul 2021 15:39:07 GMT</pubDate>
      <guid>https://rip.trb.org/View/1864509</guid>
    </item>
    <item>
      <title>An Analysis of the Agglomeration Benefits of Transit Investment</title>
      <link>https://rip.trb.org/View/1236075</link>
      <description><![CDATA[Transit investments can affect the clustering of economic activity within a region, due to the changes in accessibility that transit can provide, either by increasing firm-based access to the central business district or increasing effective labor market size. This clustering can lead to what are known as agglomeration benefits that increase overall economic productivity and are external to the decisions taken by individual firms. Cost-benefit analysis of transit investments rarely account for such external benefits. Agglomeration benefits work through several mechanisms. Two mechanisms most likely relevant to transit are knowledge spillovers enabled by firm clustering near rail stops and better labor matching, due to higher labor market access caused by expansions of transit networks. The actual linkages are complex and are not well captured by simple econometric models. The researchers propose to examine these linkages using a structural equations modeling framework that can account for the direct and indirect effects of transit investment on external productivity benefits within a region. This work will build upon a Transit Cooperative Research Program (TCRP) funded study that the research team has been engaged in. As part of this work, the researchers have developed two large datasets to examine agglomeration impacts. The first is a nationwide dataset of metropolitan areas with measures of GDP, average wages, city size, central city and urbanized area employment density, transit capacity, road capacity, and human capital. The researchers analyzed this dataset using a two-step path analysis, relying on standard cross-sectional econometric techniques to account for endogeneity. They found potentially large and significant effects of transit capacity upon agglomeration and hence productivity. The main shortcoming of this work to date is the lack of control for various other potentially confounding variables that could influence population growth and employment density and may cause inaccurate coefficient estimates for transit capacity. The researchers' theoretical framework could alternatively be implemented using structural equation modeling to account for both direct and indirect effects, providing stronger evidence of a causal relationship from transit capacity to agglomeration and from agglomeration to productivity. The second dataset consists of firm-level data for two metropolitan regions with large and growing rail transit systems: Portland, Oregon, and Dallas, Texas. Analysis of these data has found interesting differences in how transit investments in the two regions are correlated with the distribution and density of employment by industrial classification. Current analysis has aggregated firm data to the block-level rather than analyzing specific firm-level data; this will be extended by developing models at the firm level to investigate firm births and deaths, within-firm growth, and other more detailed phenomena that enable a clearer understanding of dynamics associated with agglomeration near rail stops in the two regions. Although the dataset does not include explicit productivity measures such as revenues or wages, this analysis will enable a focus on industry structure and the process by which agglomeration can lead to new firm formation, a key indicator of potential increased productivity. The researchers expect both strands of the research to provide informative results that will greatly increase the value of the work they have already completed. Assessing the costs and benefits of proposed transit infrastructure investments depends on a better understanding of how regional and firm-level productivity might be affected. The research will also have implications for decisions about funding high-speed rail service. For the New York metropolitan region in particular, investigating how regional and firm-level productivity is affected by major transit improvements is a critical missing piece for quantifying the benefits of key links such as the ARC/Gateway project.]]></description>
      <pubDate>Thu, 03 Jan 2013 15:40:36 GMT</pubDate>
      <guid>https://rip.trb.org/View/1236075</guid>
    </item>
  </channel>
</rss>